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Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value

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  • Hanyu Yang

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Zhihao Sun

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Xun Dou

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Linxi Li

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China)

  • Jiancheng Yu

    (State Grid Tianjin Electric Power Company, Electric Power Research Institute, Tianjin 300384, China)

  • Xianxu Huo

    (State Grid Tianjin Electric Power Company, Tianjin 300010, China)

  • Chao Pang

    (State Grid Tianjin Electric Power Company, Electric Power Research Institute, Tianjin 300384, China)

Abstract

Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users.

Suggested Citation

  • Hanyu Yang & Zhihao Sun & Xun Dou & Linxi Li & Jiancheng Yu & Xianxu Huo & Chao Pang, 2024. "Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value," Energies, MDPI, vol. 17(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5759-:d:1523555
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    References listed on IDEAS

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    1. Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
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